How Flipkart's Helps Small Business to Grow using NLP - FREE LIVE SESSION

Have you ever wondered how e-commerce sites such as Flipkart help small businesses grow ? Which innovation is Flipkart utilizing to execute this cycle ?

If you want to learn Natural Language Processing using Python with Flipkart Case Study. Then you can join this Free Session Conducted by us in collaboration with TechLearn. For Joining Link Scroll at the end of the article.

The internet business firm has gathered purchaser information starting around 2007 from exchanges on the site and application. On a normal day, Flipkart gathers more than 10 - 15 terabytes of information from almost 120 million clients.

For popular brands like Flipkart, natural language processing and artificial intelligence is the way forward. Flipkart leverages artificial intelligence to determine the potential customers based on product reviews.

Customer Feedbacks play a vital role in improvisation of product and services deciding the direction of business growth. Reading tons of product reviews and finding the goods and bads of a product seem daunting but thanks to Machine Learning through which Flipkart can process all reviews at once and provide necessary actions to the small businesses.

Flipkart analyzes these insights to take actions in improvising its online shopping experience, including which product it offers as well as where it should focus its innovation efforts.

Better product recommendations — Flipkart correlates user behaviour with feedback for better product recommendations on specific product aspects can help them better understand what users are looking for and recommend suitable products.

What is Natural Language Processing ?

Natural language processing (NLP) refers to the branch of computer science — and more specifically, the branch of artificial intelligence or AI — concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

Role of Natural Language Processing in AI

NLP strategies do a semantic examination of millions of client audits and concentrate valuable data out of them. The models utilize modern normal language handling calculations for angle recognizable proof, message extraction, opinion grouping, and afterward collection. First Natural Language Processing steps in the process is key expression extraction in which they distinguish examples of language containing the basic data communicated by the client.

In the following stage, they use ideas of Topic Modeling and Phrase-to-Phrase comparability to decide the item aspect explicitly being remarked on. Following this progression, they utilize a perspective based feeling scoring way to deal with convert the chose phonetic expressions into a score that can be collected across every one of the clients

Applications of Natural Language Processing –

Natural Language Processing with python may not be known widely like Data Science and Machine Learning. But we use natural language examples in everyday life. Some of the real-world examples are –

• Voice-controlled assistants like Google Assistant and Alexa.
• Customer service Chatbots.
• Streamlining the recruiting process on sites like LinkedIn by scanning through people’s listed skills and experience.
• Tools like Grammarly which use NLP to help correct errors
• Language models like autocomplete which are trained to predict the next words in a text

Introduction to Topic Modeling

Topic modeling is an unaided AI procedure that naturally recognizes subjects present in a message object and infer stowed away examples showed by a message archive. Subjects are significant words that are to the point of getting the job done the importance of the total sentence.

Since topic modeling doesn’t require training, it’s a quick and easy way to start analyzing your data. However, you can’t guarantee you’ll receive accurate results, which is why many businesses opt to invest time training a topic classification model.

How does Topic Modeling work ?

Topic modeling in python involves counting words and grouping similar word patterns to infer topics within unstructured data. Let’s take the example of Flipkart where you might want to know what customers are saying about a particular product from x seller. Instead of spending hours to find out the best-reviewed product through heaps of feedback, you can analyze them with a topic modeling algorithm.

By detecting patterns such as word frequency and distance between words, a topic model clusters feedback that is similar, and words and expressions that appear most often. With this information, you can quickly deduce what each set of texts are talking about.

What you’ll learn in this session ?

• How product review helped Flipkart to understand user behaviour?
• What is Natural language processing?
• Natural language processing applications
• Introduction to Topic Modeling
• LDA algorithm explained

Who can attend this session ?

• Anyone who is curious about data science & deep learning for Natural language processing.
• If you are into the business & marketing world - learn to use NLP to gain insight into customers & products.
• Students who are looking for a really good project on Natural Language Processing with Python.
• Python developers interested in learning how to use Natural Language Processing in AI.

So, let’s get started!!

This Course is going to amazing and it free so i don't think anyone should miss this. You will also get the Certificate of Completion.

CLICK HERE TO JOIN THIS FREE COURSE